Poverty Mapping in the Kyrgyz Republic 1

Transcription

1 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized April, 2013 Poverty Mapping in the Kyrgyz Republic 1 Methodology and Key Findings Meera Mahadevan (World Bank) Nobuo Yoshida (World Bank) Larisa Praslova (National Statistics Committee, Kyrgyz Republic) Poverty Reduction and Economic Management Unit Europe and Central Asia Region Document of the World Bank 1 This report is prepared by a team from the World Bank and the National Statistics Committee, Kyrgyz Republic. This study has been conducted under an umbrella work program, Kyrgyz Republic Poverty Analysis Program. We would like to thank Sarosh Satter (the Task Team Leader of the work program) for her continued guidance and support. We gratefully acknowledge Brian Blankespoor produced all maps and provided the market accessibility index map. We would also like to thank Aibek Balibagysh Uulu, and Aziz Atamanov for their useful comments and suggestions. We would thankfully acknowledge two peer reviewers, Umar Serajuddin and Hai-Anh Dang, for their insightful and constructive comments. Finally, we would like to thank Peter Lanjouw and Roy Van der Weide for their technical advices, particularly informing us of the latest recommendations in the World Bank s research department. Disclaimer: The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of The World Bank Group, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries. 1

2 I. Introduction 1. This report describes the process of and results from a poverty mapping exercise for the Kyrgyz Republic, using the Kyrgyz Integrated Household Survey (KIHS 2009) 2 and the Population and Housing Census (2009). 2. Poverty mapping is an exercise to estimate poverty incidence at a level where a typical household income and expenditure survey cannot produce statistically reliable poverty estimates due to high sampling errors. In the Kyrgyz Republic, official poverty rates are not produced below oblast level, as the sampling errors of the survey data become non-negligible. Various poverty mapping methodologies were devised to overcome increasing imprecision of poverty estimates as they are disaggregated. 3. The poverty mapping methodology used for this exercise is the Small Area Estimation (SAE) method developed by Elbers, et al. (2003). This methodology is one of the most commonly used poverty mapping methodologies around the world and has been widely tested and validated. The method takes advantage of the strengths of both sources: the breadth of data in the integrated survey, including expenditure data, and the size and coverage in the Census, meaning that data were collected from all households in the country as opposed to sampled from a primary sampling unit. 4. The goal for the poverty maps of the Kyrgyz Republic was to obtain poverty estimates at rayon or district level, a level at which the survey is not representative. For the purposes of this poverty map, all 7 oblasts of the Kyrgyz Republic have been considered and one out of the two cities holding a similar status to oblasts, for which data was collected in the survey.3 These include Issyk-Kul, Jalalabad, Naryn, Batken, Osh, Talas, Chui and Bishkek city. Oblasts are further disaggregated into 40 administrative units called rayons (equivalent to districts) and 13 towns4 Bishkek is also divided into 4 rayons, 5. This poverty mapping exercise has been conducted jointly with the National Statistics Committee (NSC). The World Bank team has provided hands-on training on the poverty mapping method to the NSC members. Since then, the NSC team and the World Bank team have continued to work together and have cooperated on the technical and methodological aspects of the analysis. Poverty mapping and Kyrgyzstan 6. Kyrgyzstan's landscape is blessed with a dramatic range of weather conditions and altitudes from snow-capped peaks, dry deserts, vast grasslands, to fertile farmlands. This rich terrain supports a rich diversity of plant and animal life. Kyrgyzstan is also rich in cultural diversity - the life style varies from a traditional nomadic style to the modern lifestyles in Bishkek. The landscape has shaped and preserved this nomadic lifestyle and culture for centuries while also allowing for economic growth in cities like Bishkek and Osh. Kyrgyzstan is a land-locked country surrounded by four countries: Kazakhstan in the north, 2 We use the KIHS 2009, in spite of the availability of the KIHS The 2009 KIHS was conducted the same year as the census. Poverty mapping involves the assumption that the parameters of the consumption model remain constant between the survey and the census, and only the household variables change. This assumption holds true if the survey and the census are from the same year, and this is usually given the highest preference. 3 The Government of the Kyrgyz Republic has identified Bishkek and Osh City as cities with republican subordination which, in effects, gives these two cities oblast status. 4 Throughout the report the term rayon is used both for towns and rayon for brevity. 2

3 China to the east and south-east, Tajikistan to the south-west and Uzbekistan to the west. Trade with these countries shapes economic opportunities for border areas. 7. Poverty mapping is useful for a country with strong regional diversity. For example, Atamanov et al. (forthcoming) suggests discernible welfare disparities between the capital city of Bishkek and the rural and urban areas in the rest of the country. It shows that educational attainment and household demographics differ significantly depending on the area of residence. Such diversities can be observed even among rural areas. The rural areas of Chui (in relatively close proximity to the capital, Bishkek) appear to enjoy significantly better living standards than rural areas in the rest of the country. 8. National poverty rates alone cannot tell us the living standards faced by people in different parts of the country. Disaggregated poverty indicators will help policy makers provide resources to the areas that need it the most in an objective and transparent manner. Besides poverty maps, Kyrgyzstan has other rich geo-referenced databases which include rayon level public expenditure data created by the government of Kyrgyzstan and the World Bank. Figure 1: Administrative Map of Kyrgyzstan Source: nationsonline.org 3

4 Figure 2: Elevation Map of Kyrgyzstan Source: SRTM 5 II. Methodology and Data II.1. Methodology 9. The selection of the specific poverty mapping methodology is critical; numerous methods are available and have been documented by Bigman and Deichmann (2000). An SAE method developed by Elbers et al. (2003) (henceforth referred to as ELL) has gained wide popularity amongst development practitioners around the world. 10. This Kyrgyz poverty map adopted the SAE method developed by ELL. It imputes consumption levels into census households based on a consumption model estimated from the household survey. In order for this to be possible, the consumption model must include explanatory variables (household and individual characteristics) that are available in both the census and the survey. By applying the estimated coefficients to the common variables from the census data, consumption expenditures of census households are imputed. Poverty and inequality statistics for small areas are then calculated with the imputed consumption of census households. 11. One advantage of this method is that it not only estimates poverty incidence but also estimates standard errors of poverty estimates. Since poverty estimates are computed based on imputed consumption, they cannot escape imputation errors, which are their standard errors. ELL analyzed the properties of such imputation errors in detail and derived a procedure to compute standard errors of poverty estimates. Box 1 below provides greater detail on this method. II.2. Main Data Sources and Issues faced 12. The ELL method generally makes use of household survey and population census data. The Kyrgyzstan poverty map is no exception, using the unit record Kyrgyz Integrated Household Survey 5 Jarvis, A., H.I. Reuter, A. Nelson, E. Guevara, 2008, Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database ( ) 4

5 (2009) and the Population and Housing Census (2009). 6 The household survey and the Census for the Kyrgyz Republic were from the same year, 2009, so there was some confidence that the requisite requirement for poverty mapping would be fulfilled the common variables between the survey and the census would be comparable. Box 1: The Small Area Estimation Method Developed by ELL (2003) The method proposed by ELL has two stages. In the first part, a model of log per capita consumption expenditures ( ln ) is estimated in the survey data: ych ln y ch X ch Z u ch where X ch is the vector of explanatory variables for household h in cluster c, is the vector of associated regression coefficients, Z is the vector of location specific variables with being the associated vector of coefficients, and u ch is the regression disturbances due to the discrepancy between the predicted household consumption and the actual value. This disturbance term is decomposed into two independent components: uch c ch with a cluster-specific effect, c and a household-specific effect, ch. This error structure allows for both a location effect common to all households in the same area and heteroskedasticity in the household-specific errors. The location variables can be at any level rayon and village, and can be drawn from any data source that includes all the locations in the country. All parameters regarding the regression coefficients (, ) and distributions of the disturbance terms are estimated by Feasible Generalized Least Square (FGLS). In the second part of the analysis, poverty estimates and their standard errors are computed. There are two sources of errors involved in the estimation process: errors in the estimated regression coefficients ( ˆ, ˆ ) and the disturbance terms, both of which affect poverty estimates and their levels of accuracy. ELL propose a way to properly calculate poverty estimates as well as measure their standard errors while taking into account these sources of bias. A simulated value of expenditure for each census household is calculated with predicted log expenditures X ˆ ch Z ˆ and random draws from the estimated distributions of the disturbance terms, c and ch. These simulations are typically repeated 100 times. 7 For any given location (such as a rayon), the mean across the 100 simulations of a poverty statistic provides a point estimate of the statistic, and the standard deviation provides an estimate of the standard error. 13. The census data covered roughly 1.2 million households, while the household survey covered around 4,500 households. A wide range of household information was collected including educational attainments, labor activities and occupation, residential information, and employment and housing conditions. As is the practice in all countries, the Census did not include household consumption and 6 Details of Kyrgyz Integrated Household Survey (2009) are available in Annex. 7 In the case of this Kyrgyz poverty mapping, we did not find much difference in results by increasing the number of replications. 5

6 income levels, but its wide coverage of household characteristics is an advantage for imputing household consumption precisely. II.3. Technical Challenges 14. The ELL poverty mapping methodology has been continued to be updated to improve statistical accuracy of poverty estimates in response to findings from the latest studies by experts and researchers. To this end, the World Bank research department prepares a variety of documents and manuals to inform development practitioners of the latest developments and methodological improvements in the ELL method, and they provide recommendations so that the latest findings are reflected in the ongoing poverty mapping exercise. These improvements are also reflected in the updated versions of the PovMap2 software produced by the World Bank to assist with application of the procedure. 15. The present Kyrgyzstan Poverty Mapping Exercise has faced two main technical challenges: (i) some inconsistencies in reporting between the Census and the Kyrgyz Integrated Household Survey 2009; (ii) potential over-assessment of the precision of poverty estimates; and (iii) significant differences in consumption pattern across oblasts Addressing inconsistencies between the census and the household survey 16. As per the recommendation of ELL, a national level model was created using the consumption variable from the KIHS and several household and region level variables on the right hand side, that were also present in the census. These common variables included the household size, educational level of household head and other household members, employment status of household head and other household members, employment industry, household characteristics (such as availability of electricity, sanitation facilities, size of living area and so on). 17. However, in order to generate reliable poverty maps, these variables need to have a similar mean and distribution across the census and survey. In the case of Kyrgyzstan, this was not achieved, and an important variable, household size, was different in the survey and census. As is common in some country surveys and censuses, the definition of a household differed during the implementation of the survey and census. This could explain the difference in the mean household size across the survey and census. This difference in definition also prevented a feasible adjustment procedure to the household identifier in either the survey or the census prior to implementation of the modeling and simulation exercises. This may have occurred because surveys and censuses are not necessarily designed for an exercise such as poverty mapping and often serve other purposes. For the purpose of poverty mapping, however, the difference in the way a household was defined household in the survey, and housing unit in the census also caused a number of other household level variables to not be strongly comparable across the survey and census. 18. In order to ensure robustness of the estimates, the Kyrgyz poverty mapping exercise minimizes the use of household and individual level variables, and instead uses many village aggregates of census variables to predict household expenditures. By construction, the latter group of variables is comparable in that the means and distributions are similar between the census and the survey since they are created from the census. However, a challenge of using such location aggregates of census variables is that they cannot explain variation of household expenditure within the location. Therefore, if the location is larger, its ability to explain variation of household expenditures will be limited. To minimize such a problem, the 6

7 Kyrgyzstan poverty mapping created as many village aggregates of census variables as possible to more closely predict household expenditure. 8 However, when comparable, household level variables were used. Addressing possible over-statement of the precision of estimates 19. In a recent contribution, Tarozzi and Deaton (2009) highlighted a number of concerns with the ELL methodology. Notably, they show that, under certain circumstances, the ELL method can result in an overly optimistic assessment of the statistical precision of the poverty map estimates. The present Kyrgyzstan Poverty Mapping Exercise has paid special attention to this concern and has undertaken a number of steps to address these issues. 20. The specific concerns raised by Tarozzi and Deaton (2009) can be summarized as follows. First, differences in consumption patterns can bias both poverty estimates and the standard errors. The ELL method estimates a consumption model that is assumed to apply to all households within each domain. The implicit assumption is that the relationship between household expenditures and its correlates is the same for all households within the domain, and that all remaining differences are due not to structural factors, but are attributable to errors. This is not a minor assumption and is explicitly acknowledged as such in ELL (2003) 21. Second, Tarozzi and Deaton (2009) caution that the misspecification in the error structure can lead to overstating the precision of poverty estimates. PovMap2, the software used for poverty mapping, in its current configuration can incorporate only two layers of errors (or residuals): at the levels of the household and at the level of some unit of aggregation above the household. In the case of the Kyrgyzstan poverty mapping exercise, the two layers of errors were selected to be households and the clusters (i.e., rayon). However, as noted by Tarozzi and Deaton (2008), there could be correlation in errors also at the oblast level. Tarozzi and Deaton (2009) show that if the ELL method is applied and the possible correlation of errors at these higher levels of aggregation are ignored, then standard errors on poverty estimates could be sharply understated resulting in an overly optimistic assessment of the precision of the poverty estimates. An obvious solution to this issue is to introduce multiple layers of errors during the consumption modeling. This, however, is not a practical solution given the structure of the available PovMap2 software and, more importantly, given the sampling design of most household surveys, including the Kyrgyz Integrated Household Survey. 22. Alternative remedies to resolve this issue were explored in the Kyrgyz Poverty Mapping Exercise. These are suggestive, but are not able to entirely remove the potential concern. Apart from setting the cluster level to rayon, dummies for each oblast were also included, and even interacted with rayon and household level variables. Different consumption models for each oblast 23. The latest recommendation from the World Bank research department is not to use multiple consumption models to predict household expenditures. Increasing the number of consumption models is 8 In other words, location characteristics can explain well between-location effects but cannot explain withinlocation effects. To explain the within-location effects, further disaggregated location specific characteristics need to be constructed. Location characteristics have additional benefits to minimize the risk of biases indicated by Tarozzi and Deaton (2009), as described below. 7

8 good to reflect differences in consumption patterns across areas. However, it is sometimes problematic since the number of observations for each area becomes small and, as a result, the regression coefficients become less stable. The World Bank research department now recommends one model for each poverty map and if there is any geographic variation in consumption pattern, then they recommend the use of area dummies and their interactions with other variables. 24. The Kyrgyzstan poverty mapping exercise tries to follow the recommendations as much as possible. However, analysis shows clear evidence of heterogeneity in consumption models across oblasts. Therefore, the Kyrgyzstan poverty mapping produces a separate consumption model for each oblast. This issue will be discussed in the next section where production of poverty mapping exercise is described. III. Production of Poverty Maps Creation of village level aggregates of variables in the Census 25. As stated before, household and individual level variables are not always comparable between the Census and the Survey. For example, Table 1 shows average household sizes are largely different for some oblasts such as Jalalabad, Osh, Talas, and Chui. Furthermore, the direction of difference is not uniform. For Chui oblast, the census average is smaller than the survey average. On the other hand, for Jalalabad, Osh and Talas, the census average is larger than the survey average. Such differences are also observed for other household and individual level variables. This restricts the use of household and individual level variables for predicting household expenditures. However, the use of the variable household size in a few models is justified due to the fact that the census mean still lies within the survey confidence intervals. The household size variable therefore was included in the final models unless the coefficient of the variable is statistically significant. Table 1:Average Household Size per oblast in Census and Survey CENSUS SURVEY Oblast Oblastlevel Oblastlevel Std. error 95% confidence interval Issyk-Kul oblast Jalalabad oblast Naryn oblast Batken oblast Osh oblast Talas oblast Chui oblast Bishkek city Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census To overcome the limited availability of household and individual level variables to predict household expenditure, many location specific variables are created. In Kyrgyzstan, due to confidentiality reasons, the National Statistical Office, in its goal to ensure the anonymity of respondents includes 8

9 location identifiers only down to the rayon level in the Census and household surveys. As a result, originally, this poverty mapping exercise created rayon level means of variables including household size, household education and employment variables, and other household characteristics. These census means were then merged into the survey, effectively creating an entire array of rayon level means that were entirely comparable between the survey and census because they were created in one dataset. 27. Although these rayon level aggregates of the census variables effectively solved the issue of comparability between the census and the survey, the rayons were too large (only 56 rayons exist in Kyrgyzstan) and rayon level variables thus leave a significant portion of variation in household expenditure unexplained by these models. As a result, consumption models with rayon level variables cannot produce reliable poverty estimates. 28. In response to this challenge, the Kyrgyz National Statistical Office used village level codes to create more disaggregated location variables. With this added level of disaggregation, many village level means of census variables were created and merged into the survey as well. Kyrgyzstan has only 56 rayons while it has almost 1800 villages in the Census. Even the Survey includes nearly 200 villages in the dataset. Inclusion of village level aggregates of the census variables significantly improves the ability of consumption models to explain the variation of household expenditures within rayons. One Model vs. Multiple Models 29. Following recommendations from the research group at the World Bank, only one consumption model was created to predict household expenditures. As a preliminary validation exercise for poverty mapping, poverty rates at oblast level using just one (national level) consumption model, predicted by the ELL method, were compared with those directly estimated from household expenditure in the Survey. Note that even poverty rates from the direct estimation in the Survey have sizeable standard errors at the oblast level. Therefore, if poverty rates produced by the ELL method differ significantly from the direct estimates (or lie outside their confidence intervals), this can be seen as evidence that poverty rates derived from the ELL method are likely to be far off from the true levels

10 31. Figure 3: Poverty Estimates from PovMap (National Model) vs poverty estimates from survey 32. shows results from using the ELL method for just one (national level) consumption model to estimate poverty rates at the oblast level. It appears that using just one model makes it avoidable that a few oblasts will have imputed poverty rates that are outside the confidence interval of the survey direct estimates. 10

11 Figure 3: Poverty Estimates from PovMap (National Model) vs poverty estimates from survey Oblast Conf. Interval (lower)/conf. Interval (upper) National model (village level means) Poverty Rate (survey) Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census Note: Oblast codes for this graph are as follows: 2-Issyk-Kul, 3-Jalalabad, 4-Naryn, 5-Batken, 6-Osh, 7-Talas, 8-Chui, 11-Bishkek 33. In 11

12 34. Figure 3: Poverty Estimates from PovMap (National Model) vs poverty estimates from survey 35., the red dots, representing the oblast level poverty estimates simulated by this national model are quite close to survey estimates for Issyk-Kul, Naryn, Osh, Talas and Chui. However, they are moderately different for Batken, and very different in the case of Jalalabad and Bishkek for which the national model predicts a poverty estimate outside the confidence interval of the survey estimates. Grouping Oblasts 36. When the national model was not successful, the next move was to take a step lower to oblast level. However, increasing the number of consumption models does not necessarily improve the statistical performance of poverty mapping. As the number of models rises, the sample size in the survey data for each model declines, lowering the accuracy of each consumption model. Therefore, an attempt was made to ensure that a single model had a minimum of 600 observations, to reduce the risk of high standard errors. 37. From the table above, it was inferred that the best course of action would be to construct individual models for Issyk-kul, Jalalabad, Chui and Bishkek city, and one consolidated model for Naryn, Batken, Osh and Talas. The details of the five consumption models are included in the appendix. 12

13 Table 2: No. of observations per oblast Oblast No. of Observations Issyk-Kul oblast 648 Jalalabad oblast 659 Naryn oblast 524 Batken oblast 503 Osh 9 oblast 340 Talas oblast 528 Chui oblast 646 Bishkek city 727 Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census Final Groups for Models Table 3: Model selection Model number Model #1 Model #2 Model #3 Model #4 Model #5 Oblasts Issyk-Kul Jalalabad Chui Bishkek Naryn, Batken, Osh, Talas 38. Each oblast was treated in a unique manner, expanding on its strengths. In some oblasts such as Bishkek city, household sizes remained comparable between the census and the survey, so this household variable was used. In Jalalabad, on the other hand, household sizes were not comparable so it was not used. Similarly, it was assessed what household variables were and were not comparable and an attempt was made to use as many as possible of those that were. When no plausible household level variable was comparable, the census-generated village level means were used. In this way, it was possible to capitalize on the earlier attempts at poverty mapping described above, by using information from old models to determine what was important in each oblast. Explanatory power of consumption models 39. Both R-square and Adjusted R-square statistics provide information on how well a consumption model can predict the actual consumption expenditure of each census household. Specifically, R-square is a statistic that indicates how well the predicted expenditure from a consumption model fits the actual household expenditure. The higher the R-square, the better the predicted expenditure fits the actual 9 Excluding Osh city 13

14 household expenditure. Adjusted R-square is a modification of R-square that adjusts for the number of regressors in a model. R-square always increases when a new variable is added to a model, but adjusted R-square increases only if the new variable improves the model more than would be expected by chance. 40. In the Kyrgyzstan Poverty Mapping Exercise, the adjusted R-square is generally quite high: 4 out of 5 models record an adjusted R-square of over 43 percent and one model recorded an adjusted R-square over 55 percent. For example, recent poverty mapping exercises in other countries, like Bangladesh and India, some models have the adjusted R-squares less than 40 percent. Table 4: Adjusted R2 of the different models Model number Oblasts Adj. R 2 Model #1 Issyk-Kul 56.77% Model #2 Jalalabad 48.14% Model #3 Chui 42.66% Model #4 Bishkek 36.36% Model #5 Naryn, Batken, Osh, Talas 47.48% Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census Results of Poverty Mapping 41. The results from this grouping of models were successful and the poverty estimates obtained at oblast level were not only well within survey confidence intervals but also had very low standard errors. In addition, as stated above, the models had consistently high R 2 s. Table 5: Estimated poverty rates Figure 4: Poverty estimates from PovMap (5 models) vs poverty estimates from survey Oblast Poverty Rate Std. Error. Issyk-Kul oblast 47% 0.00 Jalalabad oblast 33% 0.00 Naryn oblast 46% 0.02 Batken oblast 32% 0.02 Osh oblast 40% 0.01 Talas oblast 34% 0.02 Chui oblast 20% 0.01 Bishkek city 13% Oblast Conf. Int. (lower)/conf. Int. (upper) Mod. Conf. Int. (lower)/mod. Conf. Int. (upper) Poverty Rate (survey) Poverty Rate (model) Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census Note: Oblast codes for this graph are as follows: 2-Issyk-Kul, 3-Jalalabad, 4-Naryn, 5-Batken, 6-Osh, 7-Talas, 8-Chui, 11-Bishkek 14

15 IV. Maps 42. It is a natural perception that the incidence of poverty can vary markedly across localities within a state or even within a smaller entity such as a rayon. The KIHS data, which is representative only at the oblast level, does not allow for reliable estimates of poverty rates at the rayon level. As described in section I, the ELL poverty mapping procedure combines census and survey data to generate estimates of poverty incidence and inequality at levels of disaggregation such as the rayon. This section examines the spatial patterns of estimated poverty and how they correlate with well-known determinants of poverty like education, employment in agriculture, among others. In addition, this section provides a snapshot of poor areas through poverty maps, including distributions of the poor population. IV.1. Poverty Map Figure 5: Poverty Map and Administrative Map of the Kyrgyz Republic Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census Note: For oblast boundaries, please refer to Figure 1. The above map shows the various rayons, and and the circles depict some towns and cities that are often considered rayons themselves. The smaller circles depict selected populated areas, often rural areas. 43. Using the rayon-level poverty estimates simulated by the poverty mapping exercise, visual representation of the incidence was created on maps of Kyrgyzstan 10, using shape-files. One of its most striking features is to be able to visually depict spatial disparities that may not be obvious on examining data. 44. In the poverty map above, the darker colors indicate higher incidence of poverty. The poorer areas appear to be in the central parts of Kyrgyzstan. The border areas of Kyrgyzstan have a lower incidence of poverty, probably due to their access and connectivity with surrounding countries particularly with affluent countries such as China to the east and south-east and Kazakhstan to the north. 10 By Brian Blankespoor (DECCT, The World Bank) 15

16 The circles in the map identify populated areas most notably the capital city of Bishkek, which predictably has a high population along with a low incidence of poverty. 45. Although a poverty headcount rate is no doubt among the more important poverty statistics, it can be misleading at times for poverty alleviation purposes. For example, an area may exhibit a high poverty rate, but might be sparsely populated, resulting in few poor people residing there. In contrast, a large city might exhibit a low poverty rate, but represent a large concentration of the poor due to its high population density. For this reason, a combination of information on which places have a high degree of poverty (a map of poverty headcount rates) and where poor people live (a map of poor population) is useful for the governments and the development partners to identify most in need areas and gauge the scale of resources needed for the identified areas. 46. According to the World Development Report (WDR) of 2009, such maps not only help identify the most in need areas, but also provide useful information on urban-rural linkages. The WDR 2009 argues that, if a country has many poor people living in poor areas (usually remote and rural areas), the country, or at least the poor in the country, have not been able to fully exploit positive externalities from urban agglomeration. In contrast, if many poor people are living in rich areas (usually large cities), the country and even the poor have been able to enjoy the benefits of urban agglomeration. Figure 6: Poverty Map and Poor Population Map of the Kyrgyz Republic 16

17 Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census While the map of the distribution of the poor population above appears to be largely consistent with the poverty map (first map in Figure 6), it must be noted that while a few districts (rayons) to the east have poverty rates lower that 30%, they do have a sizeable share of the poor population. The opposite may also be true, such as in the rayon Toguz-Torous, the poverty rate is greater than 75%, but the actual poor population in absolute terms is moderate. When formulating policy, particularly for alleviating poverty, it is important to note these nuances, as looking at a poverty map may suggest a very different policy initiative than when both maps are studied in conjunction. 17

18 IV.2. Comparisons with maps of other socio-economic indicators 48. This section presents maps on the distribution of indicators that may provide some insight into welfare outcomes and access to public services. Figure 7: Market Accessibility Map and Poverty Map of the Kyrgyz Republic Source: Market Accessibility from Blankespoor (2013) and the poverty map from the World Bank and NSC team estimations. 49. The market accessibility potential index is a measure of how far an area is from a group of select major cities. In principle, the formula is a sum of population of the select major cities weighted by travel time to reach there. Therefore, the index takes a larger score if surrounding cities are larger or travel time to these cities is shorter. Geographic Information System software is used to find the shortest route and estimate travel time based on road network data. 50. Predictably, areas near Bishkek and Osh city exhibit high market accessibility since both are large cities. In contrast, central areas farthest from these cities have the lowest market accessibility. Some 18

19 mid-size cities (according to their being population centers) in the eastern areas (also due to Lake Issyk- Kul) also have high market accessibility. 51. It is interesting to note that this is largely consistent with the poverty map, with the areas around Bishkek and Osh having very low incidence of poverty. However, in terms of poor population, the numbers are quite high even in these areas, particularly around Osh city. However, this is also owing to the fact that, in general the population of these areas is quite high. 52. In the figure below, it is striking to note that the percentage of household members who are employed is lower in a few of the less poorer regions of Kygyzstan, more notably the Bishkek area. This could be because there may not be a need for too many household members to work as the working members earn enough to support the whole household. In contrast to this scenario, is the low percentage of employed household members in some poor areas such as a few central and southern districts. This may represent a shortage of employment opportunities in the region. Figure 8: Average percent of the household employed 19

20 Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census Interestingly, the regions with higher percentages of employment in agriculture also have reasonably high levels of employment among household members (percentage of household members who are employed). This corroborates the fact that agriculture, being quite labor intensive might often involve the participation of several members of the household, explaining at least in part the high percentages of family employment in agricultural areas. Figure 9: Sectors of employment: Agriculture and Trade 20

21 Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census The above figure depicts the percentage of the employed involved in trade confirms what can be seen in the market accessibility map, namely that the two big trade centers in Kyrgyzstan are around Bishkek and Osh city. 55. The figures below indicate that relatively poor regions in the center and western parts of the country are coincident to regions where there is a high percentage of population whose highest educational qualification is the completion of primary school. People with higher levels of education (tertiary) appear to be concentrated around the capital area in the north, which is consistent with it being the educational center. However, the same does not apply to Osh city where a quarter of the population has at the most completed primary education. 21

22 Figure 10: Education (primary, secondary, tertiary) Source: The World Bank and NSC team calculations based on KIHS 2009 and Populaiton Census V. Conclusion 56. This report summarizes the steps towards creating the latest poverty map of Kyrgyzstan and overlays the poverty map with maps of other socio-economic indicators. Despite some methodological challenges to constructing this map, we were able to produce it making the most of the data available to us. To overcome this limitation, several village level aggregates of the available variables from the Census were created. The more disaggregated the location variables, the more accurately household expenditure can be estimated. Therefore, the success of this new Kyrgyzstan poverty mapping exercise should rightly be attributed to the National Statistics Committee s effort to make the village codes 22

23 available for the analysis. Their effort made it possible to create these highly disaggregated means, in place of the rayon level means that were available earlier. 57. Interesting observations come out after comparing the poverty map with the other maps. Poverty incidence in border areas is in general low, with a few exceptions. This likely reflects the importance of trade with neighboring countries. Reasonably high correlation between market accessibility and poverty incidence further supports this hypothesis. A comparison between poverty incidence and poor population reveals that big cities like Osh or Bishkek exhibit low incidence of poverty, but a high concentration of poor population. This indicates that a low incidence of poverty does not necessarily imply that poverty alleviation policies are not needed, and close attention must be paid to these pockets of poverty. The correlation between education outcomes and poverty incidence is relatively limited. 58. A poverty map is a good monitoring instrument, but it becomes useful only if it is actively used for policy making. Overlaying the poverty map with maps of other socio-economic indicators makes it easy to identify what the key bottlenecks to poverty could be. Also, linking a poverty map and maps of other outcome indicators with distribution of public expenditures would be highly useful to see how outcomes and inputs are related. In fact, the Government of Kyrgyzstan and the World Bank have recently completed the creation of a public expenditure database, which is organized by the World Bank s software BOOST. This software enables one to see the linkage between distribution of the public expenditure and outcomes like poverty, making such crucial information easily accessible to policy makers. 23

24 References Atamanov, A. and S. Sattar (2012). Regional disparities report. Kyrgyz Republic Programmatic Poverty Assessment, mimeo. Bigman, D. and U. Deichmann. (2000), Spatial indicators of access and fairness for the location of public facilities, in Geographical Targeting for Poverty Alleviation. Methodology and Applications, edited by D. Bigman and H. Fofack, World Bank Regional and Sectoral Studies, Washington DC. Library of Congress, Federal Research Division (2007). Country Profile: Kyrgyzstan. Blankespoor, B. (2013). Market Accessibility and Regional Maps: Kyrgyz Republic, mimeo. Elbers, C., J.O. Lanjouw, and P. Lanjouw (2002). Micro-level estimation of welfare, Policy Research Working Paper Series no. 2911, The World Bank. Elbers, C., J.O. Lanjouw, and P. Lanjouw (2003). Micro-level Estimation of Poverty and Inequality, Econometrica, 71(1): Ibraghimova, S. (2012). Review of Sample Household and Labor Surveys in the Kyrgyz Republic, mimeo. Jarvis, A., H.I. Reuter, A. Nelson, E. Guevara (2008). Hole-filled SRTM for the globe Version 4, CGIAR-CSI SRTM 90m Database ( National Statistical Committee of the Kyrgyz Republic (2009). "Population and Housing Census of the Kyrgyz Republic of 2009," Downloaded from accessed Tolipov, F. (2011). Uzbekistan-Kyrgyzstan Relations after June 2010 Imply a Continued Lack of Regionalism, Central Asia Caucasus Institute Analyst. Tarozzi, A. and A. Deaton (2009). Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas, Review of Economics and Statistics, 91(4), World Bank (2008), World Development Report 2009: Reshaping Economic Geography, the World Bank, Washington, D.C. Yefimova-Trilling, N. (2012). Kyrgyzstan & Tajikistan: Disputed Border Heightens Risk of Conflict, Eurasianet. 24

25 Appendix 1. Model for Issyk-Kul R2= adjr2= Model variables OLS Consumption Model GLS Consumption Model Variables Coeff. Std. Err. Coeff. Std. Err. _intercept_ AGE Age HHSIZE_MEAN_2 Village level average of household size squared HH_CHILD_RATIO Ratio of no. of children to total HH members HH_EDU_DUMMY1 Ratio of no. of HH members with higher prof. degree HH_EDU_DUMMY3 Ratio of no. of HH members with secondary prof. degree HH_EDU_DUMMY4 Ratio of no. of HH members with primary vocational degree HH_EDU_DUMMY9_MEAN Village level mean of ratio of HH members who are illiterate HH_IND_COMM_MEAN Village level mean of ratio of HH members employed in commerce HH_WORKING_AGE_RATIO Ratio of no. of working age members to total HH members RAYON_2225_1 Rayon dummy SEX_1 Sex dummy _HH_EDU_DUMMY2_MEAN$RAYON_2205#0 Interaction term of education level with Rayon dummy _HH_EDU_DUMMY2_MEAN$RAYON_2210#0 Interaction term of education level with Rayon dummy _HH_EDU_DUMMY2_MEAN$RAYON_2225#1 Interaction term of education level with Rayon dummy _HH_EDU_DUMMY3_MEAN$RAYON_2205#0 Interaction term of education level with Rayon dummy _HH_EDU_DUMMY3_MEAN$RAYON_2220#1 Interaction term of education level with Rayon dummy _HH_EDU_DUMMY3_MEAN$RAYON_2225#0 Interaction term of education level with Rayon dummy _HH_EDU_DUMMY6_MEAN$RAYON_2225#0 Interaction term of education level with Rayon dummy _HH_IND_COMM$RAYON_2210#0 Interaction term of occupation (commerce) with Rayon dummy _HH_IND_FIN$RAYON_2420#0 Interaction term of occupation (finance) with Rayon dummy _HH_IND_MFG$RAYON_2210#0 Interaction term of occupation (manufacturing) with Rayon dummy Model for Jalalabad R2= adjr2= Model variables OLS Consumption Model GLS Consumption Model Variables Coeff. Std. Err. Coeff. Std. Err. _intercept_ HH_IND_DUM10 Ratio of HH members employed in unspecified activities HH_IND_FIN Ratio of HH members employed in finance HH_IND_TRADE_MEAN Village level mean of ratio of HH members employed in trade HH_IND_TRANS Ratio of HH members employed in transport HH_OLD_RATIO Ratio of over 60 (age) members to total HH members HH_WORKING_AGE_RATIO Ratio of no. of working age members to total HH members RAYON_3215_1 Rayon dummy RAYON_3223_1 Rayon dummy RAYON_3230_1 Rayon dummy RAYON_3420_1 Rayon dummy

26 SEX_1 Sex dummy SEX_MEAN Village level mean of sex dummy TOILET_MEAN Toilet dummy _HH_EDU_DUMMY1$RAYON_3225#0 Interaction term of education level with Rayon dummy _HH_EDU_DUMMY3$RAYON_3225#0 Interaction term of education level with Rayon dummy _HH_EDU_DUMMY5$RAYON_3211#0 Interaction term of education level with Rayon dummy _HH_EDU_DUMMY9$RAYON_3420#0 Interaction term of education level with Rayon dummy Model for Naryn, Batken, Osh and Talas Model variables R2= adjr2= OLS Consumption Model GLS Consumption Model Variables Coeff. Std. Err. Coeff. Std. Err. _intercept_ DWELL_OWN_3 Type of Dwelling owned HHSIZE HH size HHSIZE2 HH size squared Village level mean of ratio of HH members who have primary HH_EDU_DUMMY4_MEAN vocational degree HH_IND_MFG_MEAN Village level mean of ratio of HH members employed in manufacturing RAYON_4245_1 Rayon dummy RAYON_5236_1 Rayon dummy RAYON_6226_1 Rayon dummy RAYON_7215_1 Rayon dummy SEX_MEAN Village level mean of sex dummy _HHSIZE_MEAN$RAYON_4210#1 Interaction term of HH size village mean with Rayon dummy _HHSIZE_MEAN$RAYON_4220#0 Interaction term of HH size village mean with Rayon dummy _HHSIZE_MEAN$RAYON_4235#0 Interaction term of HH size village mean with Rayon dummy _HHSIZE_MEAN$RAYON_5258#0 Interaction term of HH size village mean with Rayon dummy _HHSIZE_MEAN$RAYON_5410#0 Interaction term of HH size village mean with Rayon dummy _HHSIZE_MEAN$RAYON_5430#1 Interaction term of HH size village mean with Rayon dummy _HHSIZE_MEAN$RAYON_6226#1 Interaction term of HH size village mean with Rayon dummy _HHSIZE_MEAN$RAYON_6255#0 Interaction term of HH size village mean with Rayon dummy _HHSIZE_MEAN$RAYON_7232#1 Interaction term of HH size village mean with Rayon dummy _HH_IND_EGW_MEAN$RAYON_4210#1 Interaction term of occupation (elec, gas & water) with Rayon dummy _HH_IND_EGW_MEAN$RAYON_5236#1 Interaction term of occupation (elec, gas & water) with Rayon dummy _HH_IND_EGW_MEAN$RAYON_7225#1 Interaction term of occupation (elec, gas & water) with Rayon dummy _HH_IND_EGW_MEAN$RAYON_7232#1 Interaction term of occupation (elec, gas & water) with Rayon dummy _HH_IND_TRADE_MEAN$RAYON_4245#1 Interaction term of occupation (trade) with Rayon dummy _HH_IND_TRADE_MEAN$RAYON_5420#1 Interaction term of occupation (trade) with Rayon dummy _HH_IND_TRADE_MEAN$RAYON_6207#1 Interaction term of occupation (trade) with Rayon dummy _HH_IND_TRADE_MEAN$RAYON_6246#0 Interaction term of occupation (trade) with Rayon dummy _HH_IND_TRADE_MEAN$RAYON_7232#1 Interaction term of occupation (trade) with Rayon dummy

27 Model for Chui R2= adjr2= Model variables OLS Consumption Model GLS Consumption Model Variables Coeff. Std. Err. Coeff. Std. Err. _intercept_ DWELL_OWN_1 Type of Dwelling owned EMP_STAT_MEAN Village mean of employment status HH_CHILD_RATIO Ratio of no. of children to total HH members HH_EDU_DUMMY1 Ratio of HH members with higher prof. degree HH_EDU_DUMMY1_MEAN Village mean of Ratio of HH members with higher prof. degree HH_EDU_DUMMY3 Ratio of no. of HH members with secondary prof. degree HH_EDU_DUMMY3_MEAN Village mean of Ratio of HH members with secondary prof. degree HH_EDU_DUMMY4 Ratio of HH members who have primary vocational degree Village level mean of ratio of HH members who have primary HH_EDU_DUMMY4_MEAN vocational degree HH_EDU_DUMMY5 Ratio of HH members who have completed secondary school HH_IND_DUM10 Ratio of HH members employed in unspecified activities HH_IND_EGW Ratio of HH members employed in elec, gas & water HH_IND_UNEMP Ratio of HH members unemployed HH_WORKING_AGE_RATIO Ratio of no. of working age members to total HH members RAYON_8203_1 Rayon dummy RAYON_8222_1 Rayon dummy RAYON_8400_1 Rayon dummy _HHSIZE_MEAN$RAYON_8203#1 Rayon dummy _HHSIZE_MEAN$RAYON_8206#1 Interaction term of HH size village mean with Rayon dummy Model for Bishkek city R2= adjr2= Model variables OLS Consumption Model GLS Consumption Model Variables Coeff. Std. Err. Coeff. Std. Err. _intercept_ AGE Age HHSIZE HH size HHSIZE2 HH size squared HH_EDU_DUMMY1 Ratio of HH members with higher prof. degree HH_EMP_STAT Ratio of employed in HH LIVING_AREA Area of living quarters _TOILET$RAYON_11204_11 Interaction term of toilet and rayon